Digital twin-based machine learning framework for predicting nonlinear seismic response of reinforced concrete shear walls using analytical data.

ABSTRACT: This study proposes a digital twin (DT) and machine learning (ML) framework to predict nonlinear pushover responses of reinforced concrete (RC) shear walls using analytically derived data. Two hundred SAP2000 layered shell models were analyzed, and monotonic lateral capacity curves were processed via SPO2FRAG for bilinear parameter extraction. Key response features-initial stiffness (K₀), yield displacement ([Formula: see text]), and post-yield stiffness ratio (α)were identified. Ten input variables including wall geometry, material properties, reinforcement ratios, axial load, and opening ratio were used to train Random Forest regressors for predicting the pushover curve descriptors. Model accuracy was validated using nested cross-validation, yielding mean test R² va

SUBMITTER: Kuria KK 

PROVIDER: S-EPMC12830832 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature